Cited by 346Advanced MRI Techniques and ApplicationsAdvanced Neuroimaging Techniques and ApplicationsFunctional Brain Connectivity Studies

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Using high-resolution quantitative mapping of R1 as an index of cortical myelination

Lutti, Antoine, Dick, Frederic, Sereno, Martin I., Weiskopf, Nikolaus·2014·NeuroImage

DOI 10.1016/j.neuroimage.2013.06.005

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Abstract

A fundamental tenet of neuroscience is that cortical functional differentiation is related to the cross-areal differences in cyto-, receptor-, and myeloarchitectonics that are observed in ex-vivo preparations. An ongoing challenge is to create noninvasive magnetic resonance (MR) imaging techniques that offer sufficient resolution, tissue contrast, accuracy and precision to allow for characterization of cortical architecture over an entire living human brain. One exciting development is the advent of fast, high-resolution quantitative mapping of basic MR parameters that reflect cortical myeloarchitecture. Here, we outline some of the theoretical and technical advances underlying this technique, particularly in terms of measuring and correcting for transmit and receive radio frequency field inhomogeneities. We also discuss new directions in analytic techniques, including higher resolution reconstructions of the cortical surface. We then discuss two recent applications of this technique. The first compares individual and group myelin maps to functional retinotopic maps in the same individuals, demonstrating a close relationship between functionally and myeloarchitectonically defined areal boundaries (as well as revealing an interesting disparity in a highly studied visual area). The second combines tonotopic and myeloarchitectonic mapping to localize primary auditory areas in individual healthy adults, using a similar strategy as combined electrophysiological and post-mortem myeloarchitectonic studies in non-human primates.

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Cortical Surface-Based Analysis
Dale, A. M., Fischl, B., & Sereno, M. I. (1999) · NeuroImage · doi:10.1006/nimg.1998.0395 · Cited by 10,910
Self-citation
A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data
Eickhoff, S. B., Stephan, K. E., Mohlberg, H., Grefkes, C., Fink, G. R., Amunts, K., & Zilles, K. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2004.12.034 · Cited by 4,033
Sequence-independent segmentation of magnetic resonance images
Fischl, B. R., Salat, D. H., van der Kouwe, A. J. W., Makris, N., Ségonne, F., Quinn, B. T., & Dale, A. M. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.016 · Cited by 2,117
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Mapping Human Cortical Areas In Vivo Based on Myelin Content as Revealed by T1- and T2-Weighted MRI
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Three‐dimensional magnetization‐prepared rapid gradient‐echo imaging (3D MP RAGE)
Mugler, J. P., & Brookeman, J. R. (1990) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910150117 · Cited by 1,276
Die Cytoarchitektonik der Hirnrinde des erwachsenen Menschen
Economo, C. V., & Koskinas, G. N. (1925) · Springer eBooks · Cited by 1,234
Visual field maps in human cortex.
Wandell, B. A., Dumoulin, S. O., & Brewer, A. A. (2007) · Neuron · doi:10.1016/j.neuron.2007.10.012 · Cited by 1,231 · cited 1×
“This has been an extremely productive approach, and has shown that much of the cortex is tiled with maps (Graziano and Aflalo, 2007; Schreiner and Winer, 2007; Wandell et al., 2007).”
FLASH imaging: Rapid NMR imaging using low flip-angle pulses
Haase, A., Frahm, J., Matthaei, D., Hänicke, W., & Merboldt, K. D. (2011) · Journal of Magnetic Resonance · doi:10.1016/j.jmr.2011.09.021 · Cited by 1,153
Actual flip‐angle imaging in the pulsed steady state: A method for rapid three‐dimensional mapping of the transmitted radiofrequency field
Yarnykh, V. L. (2006) · Magnetic Resonance in Medicine · doi:10.1002/mrm.21120 · Cited by 1,028

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Freeman, 1998, Optimisation of the ultra-fast Look–Locker echo-planar imaging T1 mapping sequence, Magn. Reson. Med., 16, 765
Hopf, 1955, Über die Verteilung myeloarchitektonischer Merkmale in der isokortikalen Schläfenlappenrinde beim Menschen, J. Hirnforsch., 46–54
Vogt, 1906, Der Wert der myelogenetischen Felder der Großhirnrinde, Anat. Anz., 24, 273

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Authors: Lutti, A., Dick, F., Sereno, M. I., & Weiskopf, N.
Affiliations: Wellcome Centre for Human Neuroimaging (GB)
University of London (GB)
Corresponding author: Lutti, Antoine
ORCID: Lutti, Antoine
Dick, Frederic
Sereno, Martin I.
Weiskopf, Nikolaus
Venue: NeuroImage
Publisher: Elsevier BV
Published: 2014-06
Volume: 93
Pages: 176-188
ISSN: 1053-8119 (ISSN-L)
DOI: 10.1016/j.neuroimage.2013.06.005
OpenAlex: W1991534565
Cited by: 346
References: 139
Citations by year: 2026: 20 · 2025: 28 · 2024: 18 · 2023: 26 · 2022: 37 · 2021: 29 · 2020: 27 · 2019: 27 · 2018: 36 · 2017: 38 · 2016: 28 · 2015: 17 · 2014: 11
FWCI: 2.84
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: NeuroImage (publishedVersion)
PubMed (publishedVersion)
BIROn (Birkbeck, University of London) (submittedVersion)
UCL Discovery (University College London) (submittedVersion)
IRIS (submittedVersion)
MPG.PuRe (Max Planck Society) (submittedVersion)
SERVAL (Université de Lausanne) (submittedVersion)
Funders: Wellcome Trust (091593)
Topics: Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Keywords: cortical myelination, myeloarchitecture, quantitative MRI, R1 mapping, cortical surface reconstruction, retinotopic map, tonotopic map, functional differentiation, non-invasive brain imaging
MeSH terms: Brain Mapping, Cerebral Cortex, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Myelin Sheath

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Using high-resolution quantitative mapping of R1 as an index of cortical myelination

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